AITAD Framework: Survey Data from 486 AIGC Users in China

486 survey responses from AIGC users in China were analyzed using structural equation modeling to test the AIGC Technology Acceptance and Diffusion (AITAD) framework. The data supports a study proposing a model for sustained AI-generated content engagement, integrating performance expectancy, effort expectancy, and innovation attributes. The dataset was authored by Ping Yang and last updated on 2026-05-07.

Use Cases

• Testing structural equation models based on constructs like performance expectancy and effort expectancy.
• Analyzing the mediating role of cognitive and emotional attitudes on continuance intention.
• Investigating the influence of innovation attributes like relative advantage and compatibility on technology acceptance.
• Studying user behavior patterns specific to the adoption and potential discontinuance of AIGC tools.

Strengths

• Data from 486 respondents provides a basis for statistical analysis.
• The underlying study employs a validated theoretical framework (UTAUT and Diffusion of Innovations).
• Files are available in documented formats (PDF, DOCX).
• Dataset is shared under a permissive CC-BY-4.0 license.

Limitations

• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• Data may reflect geographic bias inherent to its collection in China.

Provenance

Source

Ping Yang via figshare.

Collection Method

Likely collected via survey from AIGC users.

Freshness

Last updated 2026-05-07 07:53:47; freshness should be verified.

Geography

China